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Record W4386256364 · doi:10.3138/jvme-2023-0010

Training of Veterinary Students in Trans Rectal Palpation of Equids: A Comparison of Live Versus Cadaver Models

2023· article· en· W4386256364 on OpenAlexvenueno aff
Francisco José Vázquez, Laura Barrachina, Sara Fuente Franco, Cristina Manero Martinez, Antonio Romero Lasheras, Arantza Vitoria

Bibliographic record

VenueJournal of Veterinary Medical Education · 2023
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPalpationCadaverMedicineVeterinary medicinePhysical therapyAnatomySurgery

Abstract

fetched live from OpenAlex

Transrectal palpation (TP) is a basic skill in equine practice. Traditional TP learning methods include instructor-assisted TP in live animals, but this approach presents animal welfare concerns, especially when it needs to be used with large numbers of students. The main objective of this study is to compare two learning methods of TP: traditional methodology with live horses (LH) and alternative methodology using a cadaver with its flanks dissected (CDV). Twenty students with no previous equine TP experience were randomly assigned to two groups: LH ( n = 10) and CDV ( n = 10). Both groups received initial theoretical training before the LH or CDV session. Learning outcomes of both groups were assessed in a new TP session with live horses. All students were asked about their success in palpating seven intra-abdominal structures and objective ultrasound confirmation (UC) was also performed. Successful perception in palpation and by UC was similar in both LH and CDV students’ groups, without significant differences. Anonymous surveys answered by these 20 volunteers and by 126 students enrolled in the regulated course who also received this CDV training showed very positive feedback on the CDV methodology. As a limitation of the study, there were few students in each group and most of the results are based on subjective criteria. Nevertheless, we can conclude that CDV is a useful tool for teaching TP, with good learning results, allowing the instructor to see what the student is touching and avoiding the LH disadvantages. This translation was provided by the authors. To view the original article visit: https://doi.org/10.3138/jvme-2023-0010-es

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.720
GPT teacher head0.630
Teacher spread0.090 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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